Computer Based Diagnosis System for Tumor

Detection &Classification: A Hybrid ApproachVirupakshappa

1

Department of C SE, Appa Institute of Engineering &

Technology, Kalaburagi, Karnataka, India

Email: Virupakshi.108@gmail.com

Dr. Basavaraj Amarapur2

Department of E & E, Poojya Doddappa Appa College of

Engineering, Kalaburagi, Karnataka, India

Email: bamarapur@yahoo.com

Abstract: Brain tumor is one among the most dangerous diseases in

the world, patient’s life can be saved if the brain tumor is detected

and diagnosed properly in its earliest stages. Since brain has the

most complex structure in which tissues are interconnected

rigorously. Thus makes the brain tumor detection a challenging

task. Brain tumor detection and classification requires clinical

experts to meet the standard level of accuracy. This limitation is

overcome by the use of Computer Aided Diagnosis Systems (CAD

Systems) in the diagnosis of brain tumors. In this paper we propose

an efficient method for brain tumor detection and classification

using hybrid method in which segmentation is carried out using

Spatial Fuzzy Clustering, texture features are extracted using

Gabor feature extraction method and finally classification using

Artificial Neural Network (ANN) classifier. The system

performance is examined with 40 trained images with 60 tested

MRI scanned images. The comparative analysis in terms of

accuracy with reference to the confusion matrix is presented in

result section. From the experimental results we were able to

achieve proposed system’s accuracy level up to 92.5%.

Keyword: Brain tumor, MRI, CAD Systems, FCM segmentation,

Statistical and Gabor Wavelet Features Extraction, ANN Classifier.

I. INTRODUCTION

Brain tumor is the collection of uncontrolled development

of cells within the brain or spinal canal. There are mainly two

types of tumors: primary tumors and secondary tumors. Primary

tumors usually originate in the brain, wherein secondary brain

tumors originate in other parts of the body and spread to the

brain later. In most of the cases Old age people diagnosed with

brain tumor, however age is not a strict criterion for this disease.

In India, every year around 50000 people are diagnosed with the

brain tumor. Out of these figures children seldom contributes to

20 % [1]. brain tumor is claimed as second most deadly disease

after leukemia. To reduce the casualties caused by brain tumor it

is necessary to detect and diagnose the brain tumor in early

stages. Brain is very complex organ of the body; the

interconnection of tissues is very complex inside the brain, so it

is very difficult to cure the brain tumor. Since most of the

tumors vary in size, appearance, location and shape,

segmentation and classification of the brain tumor is still a

challenging job.

Imaging modalities plays an important role in the course of

brain tumor diagnosis in the patients. There are many imaging

modalities available for brain tumors, out of which Computed

tomography (CT) and Magnetic resonance image (MRI) are

highly preferred. For the examination of bone modifications

caused by brain tumors, calcifications etc. CT imaging is

preferred, to arrive any decisions regarding the brain tumors

MRI is choice of radiologist [2] because it is noninvasive and

produces high contrast images of the tissues.

In the past, lot of work has been done for the accurate

segmentation and classification of the brain tumors from the MR

Images. The segmentation approaches can be mainly categorized

into Semi-Automatic (SA) and Fully-Automatic (FA)

approaches. SA segmentation approaches require user

involvement in the detection of the brain tumors [3]. The typical

user in the SA approaches will be an expert radiologist who

makes the decisions with high degree of accuracy. Since they

analyse images visually, lot of expertise and experience is

required for the prediction of the brain tumor. Nowadays to

achieve higher accuracy, to remove ambiguity for arriving at

firm decision, most of the radiologists depend on computer aided

diagnosis systems (CAD Systems) [4]. By using CAD Systems

radiologists reinforce their decisions for prediction of the class

of the tumors. The CAD Systems contains pattern recognition

algorithms to retrieve many spectral and spatial features, which

in turn identifies the mapping among the features of the medical

images and tumor class.

CAD Systems mark the tumor regions of the image by

utilizing the implemented segmentation techniques. Various

segmentation techniques include region growing, K – Nearest

Neighbor (KNN), Markov random fields (MRF), level set

methods and fuzzy c means (FCM) [5].The region growing

International Journal of Pure and Applied MathematicsVolume 118 No. 7 2018, 33-43ISSN: 1311-8080 (printed version); ISSN: 1314-3395 (on-line version)url: http://www.ijpam.euSpecial Issue ijpam.eu

33

techniques isolates the regions with similar characteristics

defined by the user. It does well even in the presence of noise in

the image, but this method requires the selection of the seed

point manually. KNN is more sensitive to duplicated features

and known for its least run time efficiency. MRF performs well

with homogeneous tumors and this method does not segment the

heterogeneous tumor. Level set methods are very good in

segmenting the tumor boundaries except the case that it requires

initial identification of the curves. FCM identifies the initial

boundaries of the object quickly but consumes more time and

results poor in the presence of the noise in the images.

Feature extraction is a most important phase in

classification of the brain tumors, they help in differentiating the

tumors based on texture patterns and intensity values. Since

brain tumors has complex structure, it is always preferred to

extract as many features as possible. Based on the features

seldom radiologist categorizes the tumor classes. In

classification of brain tumor most of the researchers used Gabor

feature extraction method and run length matrices [6]. The visual

features help in selecting the most preferable mathematical

feature descriptors towards designing an efficient CAD Systems

for accurate classification of the tumors.

II. LITERATURE SURVEY

Ankit Vidyarthi and Namita Mittal [7] conducted

performance analysis of Gabor wavelet features in malignant

tumor classification. They used machine learning approach for

the evaluation of different features. To select the appropriate

feature set from feature vector they have included many feature

selection algorithms. They have experimented several well-

known classification methods for performance analysis of

malignant tumor classification. Various types of malignant

tumors eg. Gliomas, Glioblastoma Multiforme etc. are included

in the experimentation. Finally, they have presented

classification accuracies of different combination of feature

selection algorithms.

Mohammad Majid al-Rifaie et al [8] demonstrated the use

of swarm intelligence for recognizing the microcaclification in

mammograph, metastasis in bone marrow and segmentation of

tumor in medical images. They presented a novel deployment

method for swarm intelligence technique called as umbrella

deployment. Initially they have surveyed how this method helps

in identification of microcalcifications in mammograph images

and metastasis in the bone scans. They demonstrated the use of

proposed method in detection of nasogastric tube in the X ray

images of chest. For the segmentation of MRI brain images, they

proposed hybrid swarm intelligence learning vector

quantization.

Juan M et al [9] carried out evaluation of tumor

classification using MR Spectroscopy. A project called

eTUMOUR, which was created by the previous project called

INTERPRET facilitated such a huge evaluation. It consists of

253 pair of classifiers for metastasis, meningioma, low grade

glial and glioblastoma cases. They have achieved accuracy of

90% for acquired spectra, except the classification of metastasis

versus glioblastoma, resulting a poor classification of around

78%.

Khalid Usman and Kashif Rajpoot [10] introduced a

classification method for brain tumors by machine learning and

wavelets. They have utilized the data from MICCAI BraTS 2013

dataset, which are skull stripped and co registered and histogram

equalized. They have extracted local neighborhood, intensity,

intensity differences and wavelet texture features. Then they

supplied the combined features to the random forest classifier,

which classifies into five classes: necrosis, background, non

enhancing tumour, enhancing tumour and edema. They have

achieved accuracy of 75% for the core tumor and 88% dice

overlap for the complete tumor which is better compared to

BraTS competition.

Jainy Sachdeva et al [11] presented a novel method for

segmentation, feature extraction and classification of brain

tumor. They have worked on total of 428 T1 weighted images

collected from 55 patients. Content based active contour model

was used to extract 850 six regions of interests. From these

region of interest 216 texture and intensity features are extracted.

To reduce the dimensionality of the features, Principal

Component Analysis is used. Finally, ANN classifier is used to

classify the images into six classes with rise in the accuracy

from 77 to 91%.

Praveen B. and Anita A [12] presented a Hybrid method for

Brain Tumor identification and Classification in MR images.

Proposed method consist of four phases: in the first phase

preprocessing is carried out for skull stripping and filtering the

noise. Then by using gray level co-occurrence matrix features

were extracted in the second phase. In the third phase

classification of images into normal or abnormal using support

vector machine. Finally, tumor part is segmented using

bounding box method. They have experimented on total of 100

images in which 75 are abnormal images and 25 are normal

images.

In consideration with advantages and disadvantages of the

above mentioned methods, we are presenting a hybrid approach,

which is a collection of region based, edge based and texture

based methods for detection and classification of brain tumors

from the MR Images. In the proposed method, we begin with

preprocessing of the input images by discrete wavelet

transformation (DWT) to highlight the quality of input image.

Segmentation was carried using Spatial Fuzzy Clustering, which

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is combination of Level Set Method and Fuzzy C Means

techniques for separating the region of interest (ROI) from MR

Images. Then Texture features were extracted from ROI using

statistical features and Gabor feature extraction method. Finally,

the extracted features were fed to the Artificial Neural Network

(ANN) classifier to classify the given image into normal or

benign tumor or malignant tumor.

The organization of remaining paper is as follows. The

section “METHODOLOGY” discusses the method used for

segmentation, features vectors and classification. Section

“EXPERIMENTAL RESULTS” portrays the experimental

results and discussion of comparative analysis is performed

briefly. Finally, paper is concluded in “CONCLUSION” section.

III. METHODOLOGY

Tumor detection and its subsequent classification using

image processing is one of the extensively used disease analysis

model in clinical filed. Our proposed system architecture for

brain tumor detection is as shown in below Figure 1.1.

Figure 1.1: Proposed System Operational Block Diagram

In the proposed method, we have used the MRI brain images

collected from the local medical hospitals as well as from the

internet. The segmentation is carried out using spatial fuzzy

clustering method to detect the boundaries of the tumors,

whereas the classification is done on the basis of knowledge

base. This knowledge base is created by using or training the

number of MRI scanned brain tumor images. The brain tumor

classification model is divided into training and testing phases.

In training phase, by feeding the features to the classifier the

knowledge base is created. In testing phase the input image is

queried to the classifier for predicting the class of the image.

A discrete wavelet transformation model is applied in pre-

processing model to enhance the visual quality of the query

image. Tumor region is identified by using fuzzy c mean

clustering algorithm, which segments the input image based on

its intensity and pixel distance levels. Texture features of the

identified tumor region are collected by using Gabor wavelet

and statistical features methods. Based on the collected feature

vectors ANN classifies the input image either normal or in initial

stage of the tumor else in final stage of the tumor. The

mathematical function of the intermediate block of the proposed

system is explained in below subsection.

1. Discrete Wavelet Transformation (DWT)

DWT is one of the most significance image analysis models

designed for cascaded filtering with different sub sampling

factors. This linear transformation model functions on data

vectors these data vector is converted into a multiple numerical

with constant vector size. A DWT tree with sub sampling factor

2 is shown in below Figure1.2.

Figure 1.2: DWT Tree

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The low pass filter and high pass filters of DWT function are

represented by the variable H and L respectively. The

mathematical equations for both high and low pass filter are

given in below Eq. (1) and (2).

𝐋𝐣+𝟏 = 𝐚 𝐧 − 𝟐𝐩 𝐋𝐣 𝐧

+∞

𝐧= −∞

(𝟏)

𝐇𝐣+𝟏 = 𝐛 𝐧 − 𝟐𝐩 𝐋𝐣 𝐧

+∞

𝐧= −∞

(𝟐)

Here 𝐿𝑗 and 𝐻𝑗 termed as a wavelet coefficient used during

data transformation from one stage to other stage and compute

the transformation output. Here it is considered that out of j scale

j+1 is only half scale of the entire L and H data elements. This

process is continued until to meet remaining elements of the

signal scale. Due to operational level, these coefficients are also

termed as scaling coefficient of DWT.

Figure 1.3 present the detail explanation of the DWT data

decomposition in a 2D form. Initially data is analyzed in row

wise and further it is analyzed in column wise. Image pre-

processing using DWT function analyze the given image in

multiple resolution [13]. This increase the visual quality of the

input image, due to this reason it is mainly used with biomedical

images.

Figure 1.3: DWT Decomposition

2. Spatial Fuzzy Clustering

FCM is a clustering method used to segment the regions in

the images based on the intensity values. The accuracy of

this method will be degraded in the presence of noise and

intensity in-homogeneity in the input image.To overcome

this new features are added to the existing FCM to create a

hybrid segmentation method called as Spatial Fuzzy

Clustering. This method is combination or fusion of level set

method and Fuzzy C-Means algorithm. Spatial fuzzy

clustering isrepresented by the Eq. (3)

𝐥𝐢𝐣 = 𝐮𝐢𝐤𝐤∈𝐒 𝐲𝐣

(𝟑)

In this method a bias corrected image is passed as input,

where spatial parameter of the given input image is integrated

with fuzzy clustering. In given equation 𝑦𝑗 present the center

pixel of a square window𝑠(𝑦𝑖). The probability occurrence of

pixel 𝑦𝑗 in a 𝑖𝑡ℎ cluster is denoted by𝑙𝑖𝑗 . The majority of the

same pixel which belongs to same cluster will directly increase

the value of that pixel spatial function.

2.1 Level Set Segmentation

The dynamic variations in the boundaries are efficiently

utilized by integrating the pixel classification and level set

methods. Active contours are most flexible and well know

segmentation techniques used in clustering. These active contour

and parametric characterization is embedded by the level set

technique with time dependent PDE function. A zero level set is

tracked to approximate the changes in active contour. The zero

level set is denoted as𝜏 𝑝 .

𝛟 𝐩, 𝐲, 𝐳 < 0, 𝑦, 𝑧 𝑖𝑠 𝑖𝑛𝑠𝑖𝑑𝑒 𝐚𝐬 𝛕 𝐩 (𝟒)

𝛟 𝐩, 𝐲, 𝐳 = 𝟎, 𝐲, 𝐳 𝐢𝐬 𝐚𝐭 𝛕 𝐩 (𝟓)

𝛟 𝐩, 𝐲, 𝐳 > 0, 𝑦, 𝑧 𝑖𝑠 𝑜𝑢𝑡𝑠𝑖𝑑𝑒 𝜏 𝐩 (𝟔)

The integration of fuzzy clustering and level set method can

efficiently handle different dimensional images. The application

of level set algorithms can efficiently segment the given image

especially medical images.

2.2 Fuzzy C Mean Clustering

FCM is one of the clustering method which segments the

object of interest by forming the clusters. The FCM

segmentation method is totally different from other methods in

the sense the data membership to each cluster is not fixed in

FCM. To overcome this problem, the FCM method assigns data

membership degree to each cluster. The accuracy of the

algorithm is measured by the number of iterations required to

segment the image. [14].

During in intermediate iteration, J denotes the objective

function which decreases in every iteration level. The

mathematical equation of objective function is as shown in Eq.

(7).

𝐉 = 𝛅𝐢𝐣 ∥ 𝐱𝐢 − 𝐜𝐣 ∥𝟐 (𝟕)

𝐂

𝐣=𝟏

𝐍

𝐢=𝟏

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The maximum numbers of data elements to be clustered are

denoted by N and the required maximum cluster groups are

denoted by C. For 𝑖𝑡ℎ data point 𝑥𝑖 the degree of data

membership and centre vector for cluster j is represented as 𝛿𝑖𝑗

and 𝑐𝑗 . In above equation ∥ 𝑥𝑖 − 𝑐𝑗 ∥ defines the data point

closeness to the 𝑐𝑗 and centre vector of the 𝑗𝑡ℎ cluster. By using

the available data points 𝑥𝑖 , the degree of data membership of

the respective j cluster is computed by using below Eq. (8)

𝜹𝒊𝒋 =𝟏

∥𝒙𝒊−𝒄𝒋∥

∥𝒙𝒊−𝒄𝒌∥

𝟐

𝒎−𝟏𝑪𝒌=𝟏

(𝟖)

FCM fuzzy coefficients are represented by m in above Eq.

(8).Further a centre vector for each cluster is computed as

𝒄𝒋 = 𝜹𝒊𝒋

𝒎𝒙𝒊𝑵𝒊=𝟏

𝜹𝒊𝒋𝒎𝑵

𝒊=𝟏

(𝟗)

𝛿𝑖𝑗 Is computed by using above Eq.(8). Initially the date

membership degree for each cluster is defined randomly i.e.𝜃𝑖𝑗

between 0 ≤ 𝜃𝑖𝑗 ≤ 1 such that 𝛿𝑖𝑗 = 1𝑐𝑗 . The tolerance

between the clustering is measured by fuzziness coefficient m

i.e. 1 < 𝑚 < ∞. The clustering overlap is identified by this term,

higher the value increases the cluster overlap, along with the

data degree of membership is within 0 to 1. The functional

algorithm of FCM clustering presented

Algorithm 1: FCM Clustering

1. For defined number of clusters c , initialize the data

degree of membership δij with selected m value and xi

data points to meet the below condition 𝛅𝐢𝐣 = 𝟏𝐜𝐣

2. computer fuzzy cluster centre

𝐜𝐣 = 𝛅𝐢𝐣

𝐦𝐱𝐢𝐍𝐢=𝟏

𝛅𝐢𝐣𝐦𝐍

𝐢=𝟏 where i = 1,2,3...c

3. Update the fuzzy data membership for each cluster

using eq.

𝛅𝐢𝐣 =𝟏

∥𝐱𝐢−𝐜𝐣∥

∥𝐱𝐢−𝐜𝐤∥

𝟐

𝐦−𝟏𝐂𝐤=𝟏

4. check the object function,

if less than predefined threshold

stop function

else

goto step 2.

end

3. Feature Extraction

Collection of features are called as Feature Vectors, these

vectors always has great influence towards image analysis and

classification. In the proposed methodology, texture features are

extracted using two methods: Statistical and Gabor features

extraction method.

Statistical Features

The texture statistical features of the input image are determined

by considering either histogram of the input image or by

generating the co -occurrence matrix of the input image. In the

proposed system we are going to collect the statistical features

by generating the co – occurrence matrix of the input image.

Using these features mean, standard deviation andvariance is

calculated. The operational flow statistical features are shown in

Figure 1.4.

Start

Input Binary Image

Compute Size of Input Image

Construct Co-occurrence

Matrix for Angle 0, 45, 90 and

135

Compute Average of Mean of

All Co-occurrence Matrix

Compute Mean of GLCM

Compute Standard Deviation

Compute Variance

Compute Range Values

Find Maximum Range

Store All Statistical Feature in

a Single Vector

Stop

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Figure 1.4: Function Flow Statistical Feature Collection.

Gabor Wavelet Feature Extraction

The segmented 2D tumor texture features are collected using

Gabor wavelet algorithm. It applies the complex Fourier

transformation algorithms for signal analysis of the input data.

The 2D Gabor kernels used in proposed system is

𝐖 𝐱, 𝐲,𝛉,𝛌,𝛗,𝛔,𝛄 = 𝐞𝐱𝐩 −𝐱′𝟐 + 𝛄𝟐𝐲′𝟐

𝟐𝛔𝟐 𝐜𝐨𝐬

𝟐𝛑𝐱′

𝛌+ 𝛗

…… . (𝟏𝟎)

Where: 𝜆,𝜑,𝜎, 𝛾 is wavelet parameters preferred during feature

collection

𝐱′ = 𝐱 𝐜𝐨𝐬 𝛉 + 𝐲 𝐬𝐢𝐧 𝛉 (𝟏𝟏)

𝐲′ = −𝐱𝐬𝐢𝐧 𝛉 + 𝐲 𝐜𝐨𝐬 𝛉 (𝟏𝟐)

The light impulse of visual filed is specified by (x, y) [15].

4. ANN Classifier

Once the features are extracted, will be fed to the classifiers

for the prediction of the class of the image. In the proposed

method all the texture features are fed to the ANN classifier for

further classification. The ANN classifier has three tiny modules

which can operate independently.

Figure 1.5: Intermediate Operational Block of ANN Classifier

These independent functional elements of ANN are known

as Neurons. The figure 1.5 shows the interconnection between

the neurons. The ANN classifier produces the output if and only

if node’s output is positive, this output is produced by the

product of the input sample value with classifier weight and then

sum of the multiplication is added with respective bias. In this

proposed system classifier is designed in such way that it should

classify the tumor into three categories, i.e. normal image,

Benign tumor or malignant tumour. ANN model is simple and

its mathematical equation is presented in Eq. (13)

𝐲 𝐤 = 𝐅 𝐰𝐢

𝐦

𝐢=𝟎

𝐤 ∗ 𝐱𝐢 𝐤 + 𝐛 (𝟏𝟑)

Where Input signal value at k discrete time is represented

byxi(k), weight signal values at k discrete time is represented

bywi(k), b denotes the bias and transfer function presented with

F function and lastly output values for k discrete time is shown

in yi k [16].

IV. EXPERIMENTAL RESULTS

The performance of the proposed method is tested on the

standard dataset using ANN classifier. The proposed method is

implemented in MATLAB 2012a tool. We have experimented

on standard MRI images in both training phase and testing

phase.The input images are presented in Figure 1.6(a) and the

quality of the given image enhanced by the application of

preprocessing technique. The enhanced brain image is presented

in Figure1.6(b). Figure 1.6 (c) and (d) shows the segmentation

output. The application of Spatial fuzzy clustering algorithm

finds the presence of tumor in the given input image.Statistical

and Gabor features extraction techniques collectthe texture

information of the segmented tumor region. These features are

passed on to ANN classification section to identify the stage of

tumor.Based on similarity between the trained features, ANN

will classify the input image into the respective tumour classes.

International Journal of Pure and Applied Mathematics Special Issue

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(a) (b) (c) (d) (e)

Figure 1.6: (a) Input Image; (b) DWT Pre-processed Image; (c) FCM Clustered Output; (d) Tumor Recognition; (e) Tumor Stage Classification

The performance of the proposed system is examined by

considering segmentation accuracy and data classification

accuracy. Segmentation accuracy is the rational of set of pixels

that are classified correctly to the number of pixels. Table 1

presents the segmentation comparison table of proposed system

with existing approaches.

Table 1: Segmentation Comparison Table of Proposed System with Existing Systems

Sl. No Paper Methods Segmentation Accuracy (%)

1 Vida Harati et.al [17] Fuzzy Connectedness Algorithm 92.89

2 Baida Nath Saha et.al [18] Mean Shift Clustering (MSC) 92

3 Ankur Jyoti Das et.al[19] Morphological Operations and K-Means

Segmentation 89.4

4 S. K. Nayak et.al[20]

FLICM 89.26

KFLICM 89.41

WFLICM 82.75

KWFLICM 90

5 Ali Isin et. at [21] CNN 77

6 Sharvan Rao et.al [22]

K – Means 60

Fuzzy – C 73.33

Adaptive - K 88.67

7 Proposed System SFCM 94.32

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Sensitivity and specificity are the two functional parameter

used to measure the position and negative condition of the

proposed system result. The graphical representation of the

system segmentation accuracy is as shown in Figure 1.7 and

similarly sensitivity and specificity graph is shown in Figure 1.8.

𝐒𝐞𝐧𝐬𝐢𝐭𝐢𝐯𝐢𝐭𝐲 = 𝐓𝐏

𝐓𝐏 + 𝐅𝐍 (𝟏𝟒)

𝐒𝐩𝐞𝐜𝐢𝐟𝐢𝐜𝐢𝐭𝐲 = 𝐓𝐍

𝐓𝐍 + 𝐅𝐏 (𝟏𝟓)

Table 2:Sensitivity and Specificity Comparison Table of Proposed System and Existing System

Sl.

No Paper

Methods Sensitivity Specificity

Classification

Accuracy (%) Segmentation Classification

1 Selvaraj Damodhram et.al

[23]

Region Prop

Algorithm

KNN 1 0.6 67

Neural Network 1 0.75 83

Bayesian 0.67 0.67 67

2 A. Shenbagarajan et. al [24] Active Counter

Method

SVM 0.89 0.94 86.50

KNN 0.86 0.89 91.14

3 Sathya Subramaniam et. al

[25]

Region Growing

Algorithm

Neural Network 0.69 0.75 74

Neural Network +

BCO 0.70 0.79 76

5 Proposed System SFCM ANN 0.90 0.94 92.56

Table 2 presents the proposed system sensitivity and

specificity comparison table with existing system output. The

mathematical equation for this computation is given in Eq. 14

and 15.

International Journal of Pure and Applied Mathematics Special Issue

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Figure 1.8: Proposed and Existing Comparison Graph for Sensitivity and Specificity

Further the proposed system’s classification accuracy is

compared with the existing classification methods.

V. CONCLUSION

Brain tumor identification and its subsequent classification is

a most challenging task. In this paper, we have presented a

hybrid method for brain tumor detection and classification. The

experimental results proved the performance of the proposed

method better than the previous methods. We have achieved the

classification accuracy of 92.5%. The classifier’s output helps

the radiologist to make the decisions without any hesitation. In

future we are planning to extract more number of features so that

the accuracy of the classifier can be improved further.

ACKNOWLEDGEMENTS

The authors would like to thank Dr. Nagendra Patil, H O D

of Radio Diagnosis K B N Institute of Medical Sciences

Kalaburagi, for the validation of the obtained results with respect

to the ground truth samples.

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Sensitivity and Specificity Comparision

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